ENHANCING REMOTE TRANSMISSION OF PHYSIOLOGICAL SIGNALS USING SUPERVISED AND UNSUPERVISED MACHINE LEARNING TECHNIQUES
The remote surveillance of physiological parameters, including Electrocardiogram (ECG), Blood Pressure (BP), and Photoplethysmography (PPG), is essential to contemporary healthcare, especially in the management of chronic diseases and post-operative care. Nonetheless, transferring these signals across networks has issues with data compression, security, error control, and capacity optimization. This paper examines the function of machine learning (ML), particularly supervised and unsupervised methodologies, in tackling these difficulties. Supervised learning techniques, such as Random Forests, Support Vector Machines (SVMs), and neural networks, exhibit robust proficiency in data compression, error detection, signal reconstruction, and Quality of Service (QoS) forecasting. Simultaneously, unsupervised learning methodologies like K-Means, PCA, and autoencoders demonstrate proficiency in feature extraction, anomaly detection, and grouping, facilitating efficient signal transmission and adaptive network optimization. Hybrid techniques that integrate both paradigms provide synergistic advantages by flexibly adjusting to changing network and data conditions while preserving diagnostic precision. Experimental findings utilizing multi-sensor datasets underscore the efficacy of supervised models (e.g., k-NN, SVM, XGBoost) and unsupervised clustering in categorizing activities and blood pressure classifications. The performance evaluation utilizing SHAP analysis finds weight, exercise, and blood pressure measures as essential predictors. Results indicate that XGBoost surpasses traditional classifiers, attaining enhanced accuracy and resilience, while clustering techniques uncover intrinsic patterns in physiological data across various activities. Notwithstanding these advancements, obstacles persist, including the necessity for labeled datasets in supervised learning and interpretability concerns in unsupervised methodologies. Future directions highlight the advancement of robust hybrid frameworks, the incorporation of reinforcement learning for adaptive optimization, and the utilization of federated learning for safe, decentralized model training. This work highlights the revolutionary capacity of machine learning in physiological signal transmission, providing scalable, reliable, and secure methods for remote healthcare monitoring and tailored therapy.
Brain Tumor, MRI, Machine Learning, Convolutional Neural Networks, Gradient Boosting, Support Vector Machines, K-Nearest Neighbors, Random Forest, Tumor Classification, Deep Learning, Medical Imaging.